SOTAVerified

Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

Showing 58515900 of 17610 papers

TitleStatusHype
Routing Networks and the Challenges of Modular and Compositional ComputationCode0
Pre-Training a Graph Recurrent Network for Language RepresentationCode0
Refining the Responses of LLMs by ThemselvesCode0
More Expressive Attention with Negative WeightsCode0
MoRE-LLM: Mixture of Rule Experts Guided by a Large Language ModelCode0
More RLHF, More Trust? On The Impact of Preference Alignment On TrustworthinessCode0
Establishing Vocabulary Tests as a Benchmark for Evaluating Large Language ModelsCode0
More Room for Language: Investigating the Effect of Retrieval on Language ModelsCode0
Morfessor FlatCat: An HMM-Based Method for Unsupervised and Semi-Supervised Learning of MorphologyCode0
Learning to Verify Summary Facts with Fine-Grained LLM FeedbackCode0
MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized CollaborationCode0
LLM-enhanced Self-training for Cross-domain Constituency ParsingCode0
The Crucial Role of Samplers in Online Direct Preference OptimizationCode0
Representation of linguistic form and function in recurrent neural networksCode0
Quasi-Recurrent Neural NetworksCode0
Representation Learning of Daily Movement Data Using Text EncodersCode0
QUDEVAL: The Evaluation of Questions Under Discussion Discourse ParsingCode0
Round Trip Translation Defence against Large Language Model Jailbreaking AttacksCode0
HSI: Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language ModelsCode0
Muppet: Massive Multi-task Representations with Pre-FinetuningCode0
QueerBench: Quantifying Discrimination in Language Models Toward Queer IdentitiesCode0
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class ImbalanceCode0
Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled SetsCode0
The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model PretrainingCode0
Recurrent Memory Networks for Language ModelingCode0
Representation Degeneration Problem in Training Natural Language Generation ModelsCode0
Language Model Guided Interpretable Video Action ReasoningCode0
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural InformationCode0
TULUN: Transparent and Adaptable Low-resource Machine TranslationCode0
MuseChat: A Conversational Music Recommendation System for VideosCode0
The Effectiveness of Masked Language Modeling and Adapters for Factual Knowledge InjectionCode0
NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classificationCode0
The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze TaskCode0
The effect of fine-tuning on language model toxicityCode0
Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and OccupationsCode0
Learning Better Masking for Better Language Model Pre-trainingCode0
Rotational Unit of MemoryCode0
The Effects of In-domain Corpus Size on pre-training BERTCode0
Logical Implications for Visual Question Answering ConsistencyCode0
The emergence of number and syntax units in LSTM language modelsCode0
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuningCode0
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language ModelsCode0
LLM-GEm: Large Language Model-Guided Prediction of People’s Empathy Levels towards Newspaper ArticleCode0
Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal CluesCode0
"I've Heard of You!": Generate Spoken Named Entity Recognition Data for Unseen EntitiesCode0
The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented InterventionCode0
Learning Python Code Suggestion with a Sparse Pointer NetworkCode0
Letter-Based Speech Recognition with Gated ConvNetsCode0
Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-raysCode0
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization PathsCode0
Show:102550
← PrevPage 118 of 353Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified